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Understanding and prediction of quantum materials via modelling and computation

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Abstract

This article provides a short review of interesting results on application of modelling and computation in understanding and prediction of quantum materials. Modelling and computation are used in understanding structure–property relationship, designing novel functionality in existing materials and prediction of new materials with targeted properties. Examples are drawn from applications in uncovering structure–property relation in high Tc cuprate superconductors, low-dimensional quantum spin systems, engineering cooperative spin crossover phenomena in magnetic hybrid perovskites and coordination polymers, and machine learning assisted prediction of magnetic double perovskites and permanent magnets.

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Figure 1

adapted from reference [6].

Figure 2

adapted from reference [10]. (b) The comparison of calculated magnetic susceptibility considering different spin models (shown in lines with different styles) in comparison to experimentally measured data (shown in circles) for Na2V3O7. Figure adapted from reference [11]. (c) Calculated spin wave spectrum for Zn2VO(PO4)2. The colour variation defines the strength of magnetic structure factor S(Q,ω) with lowest strength coloured as black and highest strength coloured as white. Figure adapted from reference [12].

Figure 3

adapted from reference [20]. (b) The crystal structure (top panel) and predicted combined temperature–pressure induced spin-state transitions in bimetallic Fe-Nb coordination polymer with S = 0 low-spin (LS) state of Fe, S = 1 intermediate spin (IS) state of Fe with parallel alignment between Fe and Nb spins, HS-1 state with Fe at S = 2 HS state antiparallely aligned to Nb spin and HS-2 state with Fe at high-spin (HS) S = 2 state parallely aligned to Nb spin. Figure adapted from reference [21].

Figure 4

adapted from reference [23]. (b) DFT calculated electronic and magnetic states of predicted double perovskite compounds. Figure adapted from reference [23]. (c) Machine learning assisted prediction of low-cost rare-earth-transition metal-based permanent magnets (PM). Figure adapted from reference [24].

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Acknowledgements

The author acknowledges J. C. Bose National Fellowship (grant no. JCB/2020/000004) for funding. The author gratefully acknowledges contributions from O K Andersen, O Jepsen, E Pavarini, I Dasgupta, R Valenti, H Das, C Gros, S Kar, H Banerjee, S Chakrabarty, P M Oppeneer, K Tarafder, S Kanungo, A Halder, A Ghosh and S Rom for the results discussed in this short review article.

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  1. Department of Condensed Matter Physics and Materials Science, S. N. Bose National Centre for Basic Sciences, Kolkata, 700106, India

    TANUSRI SAHA DASGUPTA

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  1. TANUSRI SAHA DASGUPTA
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Correspondence to TANUSRI SAHA DASGUPTA.

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This article is part of the special issue on ‘Quantum materials and devices’.

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SAHA DASGUPTA, T. Understanding and prediction of quantum materials via modelling and computation. Bull Mater Sci 44, 270 (2021). https://doi.org/10.1007/s12034-021-02588-y

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